Aug 2026· Italian National Conference on Sensors· Vol 26· 0 citations· 95 references
Medicine
Abstract
Power transformers are critical components of modern power grids, and their operational reliability directly affects power system security, stability, and continuity. With the increasing intelligence and complexity of power systems, condition monitoring and fault diagnosis of transformers have received growing attention. However, conventional diagnostic methods often face limitations such as complex modeling procedures, high computational costs, weak adaptability, and insufficient generalization under nonlinear and coupled operating conditions. In recent years, surrogate models have emerged as effective tools for transformer fault diagnosis because of their advantages in high-dimensional nonlinear mapping, rapid prediction, and data-driven approximation. This paper systematically reviews the research progress of surrogate models in transformer fault diagnosis and establishes a classification framework from the perspectives of model types, modeling strategies, data sources, and application scenarios. The principles, applicable conditions, and performance characteristics of representative surrogate models are comparatively analyzed. Furthermore, the advantages and limitations of different surrogate modeling approaches are discussed in terms of diagnostic accuracy, stability, generalization ability, interpretability, and computational efficiency. Although surrogate models show strong potential for intelligent transformer fault diagnosis, challenges remain in small-sample learning, data quality dependence, model interpretability, and cross-condition generalization. Future research should focus on multi-source information fusion, integration of physical mechanisms with data-driven learning, lightweight intelligent modeling, and standardized evaluation systems. This review aims to provide methodological guidance and technical references for the development of reliable, efficient, and interpretable transformer fault diagnosis methods.
Power transformers are among the most critical components of electric power transmission and distribution systems, and unexpected failures can lead to substantial economic losses and prolonged power outages. Dissolved Gas Analysis (DGA) is the most widely used diagnostic technique for transformer fault diagnosis and involves interpreting gases dissolved in insulating oil. However, conventional interpretation methods, such as the Rogers ratio, the Doernenburg ratio, the IEC 60599 ratio method, and the Duval Triangle, rely heavily on expert knowledge, may produce inconsistent diagnoses for the same oil sample, and may fail to provide a diagnosis in certain cases. In this study, 12 classification models were evaluated using the publicly available Power Transformers Fault Detection and Diagnosis (FDD) and Remaining Useful Life (RUL) dataset and a unified evaluation protocol. Model performance was assessed using Accuracy, Balanced Accuracy, and Macro-F1 score, while model interpretability was investigated through Shapley Additive Explanations (SHAP) analysis. The results showed that ensemble tree-based methods achieved the best overall performance. LightGBM and Random Forest both attained an Accuracy of 0.969 and a Macro-F1 score of 0.924, while LightGBM further achieved the highest Balanced Accuracy of 0.940. XGBoost exhibited the most stable performance under cross-validation. SHAP analysis revealed that engineered relative concentration features, particularly the CO/H2 ratio and the combined gas ratio, were among the most influential features for fault classification. These findings demonstrate that, for datasets of this scale, ensemble tree-based models combined with well-designed features provide strong and interpretable performance for imbalanced DGA-based fault diagnosis, highlighting the effectiveness of feature-based ensemble learning for small- to medium-sized datasets.
Renewable energy systems, including solar photovoltaic arrays and wind turbines, operate under highly variable environmental and operating conditions. Factors such as changing irradiance, temperature fluctuations, wind variability, and component aging make Fault Detection and Diagnosis (FDD) particularly challenging. Therefore, developing reliable, accurate, and interpretable diagnostic methods is essential to ensure system efficiency, safety, and long-term operation. Traditional model-based approaches, which rely on physical system models, offer clear interpretability and solid theoretical foundations. However, their effectiveness can be limited by modeling inaccuracies and difficulties in capturing complex nonlinear behaviors. On the other hand, data-driven and Artificial Intelligence (AI) techniques have demonstrated strong capabilities in pattern recognition and fault classification, but often face challenges related to data dependence, limited transparency, and reduced robustness under unseen conditions. This paper provides a comprehensive and structured review of FDD techniques for renewable energy systems, covering model-based, signal-based, data-driven, and hybrid approaches. A unified perspective is presented to clarify the strengths, limitations, and application domains of each category. Particular attention is given to recent advances in hybrid methods that combine physical modeling and AI, including feature fusion, ensemble learning, attention-based models, and transfer learning. Moreover, advanced signal processing techniques are discussed for their role in extracting meaningful features from noisy and non-stationary data. Rather than ranking methods by headline accuracy, which has become saturated and is only weakly comparable across heterogeneous datasets, the review adopts a critical, deployment-oriented perspective that emphasizes cross-condition robustness, standardized benchmarking, and the constraints of real-world deployment. The review also highlights the growing importance of digital twin technology as a promising framework for next-generation FDD systems, enabling real-time monitoring, adaptive learning, and predictive maintenance. Furthermore, Explainable AI is explored as a key direction for improving the transparency and trustworthiness of AI-based diagnostic models. Finally, the paper identifies major challenges and open research issues, such as data scarcity, generalization among different operating conditions, computational efficiency, and system reliability. Future research directions are outlined toward developing more robust, adaptive, and interpretable FDD solutions that can operate effectively in dynamic and uncertain environments.
Marouane Marzouk, Majdi Mansouri, Ahmed Anis Kahloul et al.· IEEE Access· 1 citation
With the large-scale integration of new energy into power systems, the intermittency and volatility caused by the high penetration of renewable energy generation (such as wind power and photovoltaic power) require diagnostic systems to possess strong uncertainty-handling capabilities. Consequently, the complexity and uncertainty of power grid operation have increased significantly, posing unprecedented challenges to the safe, stable and economic operation of the power system. The traditional fault diagnosis and handling methods, which are based on fixed models and manual experience, can no longer adapt to the dynamic and complex operating characteristics of the new energy power grid, making it urgent to explore intelligent technical solutions. Traditional power grid fault diagnosis approaches rely mainly on expert experience and physical models. Model-based methods locate faults through state estimation and power flow calculation, whose accuracy heavily depends on model precision and parameter identification. However, under complex operating conditions such as high new energy penetration, grid topology changes, and frequent fluctuations in power supply and demand, establishing an accurate mathematical model that can cover all operating scenarios is extremely challenging—model mismatches often occur, leading to reduced fault diagnosis accuracy. Expert systems, on the other hand, integrate the operational experience of power grid engineers into rule bases, offering transparent reasoning processes that are easy to understand and verify. Yet, they suffer from inherent limitations: knowledge acquisition is time-consuming and labor-intensive, it is difficult to update rules in a timely manner with the iteration of grid technology, and they lack self-learning ability, making it impossible to adapt to new fault types and complex operating environments brought by new energy integration. When dealing with massive real-time data generated by the power grid (including new energy output data, load data, equipment monitoring data, and environmental data) and complex system environments, the following prominent problems frequently arise, which further restrict the efficiency and reliability of power grid operation and fault handling.
Rui-Ze Ji· Highlights in Science Engine...· 1 citation
Aircraft landing gear systems are characterized by strong nonlinearity, multi-physics coupling, and significant uncertainty. This paper reviews representative modeling approaches, including multibody dynamics, rigid–flexible coupling modeling, multi-domain unified modeling, co-simulation, and tire–runway coupled modeling. Furthermore, the applicability of fault tree analysis, bond graph-based diagnosis, data-driven diagnosis, and model-data fusion diagnosis is examined. The results indicate that physics-based models provide strong interpretability and support airworthiness verification; however, they involve high modeling costs and perform poorly in real time under complex operating environments and parameter uncertainties. Purely data-driven methods excel at extracting nonlinear features but are constrained by fault sample scarcity and the long-tail distribution of fault modes. Fusion diagnosis, digital twin technology, and hardware-in-the-loop (HIL) validation are regarded as promising solutions for balancing accuracy, interpretability, and engineering feasibility. Future research should focus on high-fidelity reduced-order modeling, intelligent diagnosis under limited samples, lifecycle-oriented digital twins, and airworthiness-oriented validation frameworks to support the design, health monitoring, and predictive maintenance of large civil aircraft landing gear systems.
Zhu Cheng· Applied and Computational En...· 0 citations
Power Flow (PF) analysis is a fundamental tool in distribution systems since it determines the steady-state operating point for specified input conditions. Modern distribution networks face high distributed energy resource (DER) penetration and variable operating conditions, requiring repeated PF evaluations in time-series and scenario-based studies. Their unbalanced operation and high R/X ratios can challenge conventional PF solvers, thereby requiring accurate, robust, and scalable methods. Prior studies have examined nonlinear distribution PF solvers and uncertainty-based formulations, but the review literature remains limited to specific categories and lacks a unified discussion of linearized models, numerical robustness, and acceleration techniques. Therefore, this paper presents a state-of-the-art review of PF methods for modern distribution networks, covering 205 studies published between 2000 and 2026. It summarizes conventional nonlinear PF formulations, reviews linearized models with their assumptions and applicability, and surveys numerical robustness strategies for improved convergence. The methods are compared according to their applicability to radial, weakly meshed, and unbalanced networks, while practical selection criteria are provided based on accuracy, convergence reliability, and computational requirements. Probabilistic, interval, and fuzzy approaches are also reviewed under renewable and load uncertainty. Finally, acceleration strategies for repeated PF evaluation are discussed, emphasizing sparse numerical implementations, topology-based schemes, and physics-informed surrogate models.
Ayesha, G. Mosaico, Federico Silvestro· Energies· 0 citations
The results indicate that phase-angle information provides supplementary and class dependent discriminative value, but does not consistently improve all fault classes, whereas conventional voltage and current measurements alone represent a simpler and more stable alternative, whereas phase-angle measurements may be incorporated when synchronized phasor information is already available.
Zeynep Bala Duranay, İsmail Anıl Avcı, Mohammed Bushra Mohammed et al.· Symmetry· 0 citations
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